✅ Full project structure
✅ Detailed task list (GitHub issues style)
✅ ML tasks
✅ News/Sentiment tasks
✅ TA/Divergence tasks
✅ Execution engine
✅ Backtesting engine
✅ Training schedule & pipelines
✅ Additional upgrades you didn’t ask for (institutional-grade improvements)

📌 GITHUB PROJECT ITINERARY / ROADMAP
“AI-Driven Algorithmic Trading System”

📁 1. PROJECT STRUCTURE
/ai-algo-bot
│
├── /data
│ ├── raw/
│ ├── processed/
│ ├── news/
│ ├── economic_calendar/
│
├── /ml
│ ├── model.py
│ ├── train.py
│ ├── dataset_builder.py
│ ├── features.py
|
├── /strategy
│ ├── indicators.py
│ ├── divergence.py
│ ├── risk.py
│ ├── signal_engine.py
|
├── /execution
│ ├── mt5_executor.py
│ ├── binance_executor.py
│ ├── order_manager.py
│
├── /news
│ ├── news_scraper.py
│ ├── sentiment.py
│ ├── calendar_scraper.py
│
├── /backtest
│ ├── backtester.py
│ ├── performance.py
│
├── /deployment
│ ├── vps_setup.sh
│ ├── cron_jobs.sh
│ ├── dockerfile
│
├── config.json
├── main.py
└── README.md

🧩 2. CORE MODULES TO BUILD (GITHUB TASK LIST)
Below is the list EXACTLY how you would create GitHub Issues.

📌 Issue 1 — Data Pipeline
Tasks:

Connect to exchange API (MT5, Binance, Bybit)

Pull historical OHLCV data

Store raw OHLCV in /data/raw/

Build processing pipeline (resample 1m → 5m → 15m)

Add technical indicator generation

Add normalization/scaling methods

Save processed datasets for ML

📌 Issue 2 — Technical Indicators Engine
Tasks:

Build EMA 50/200

Build ATR

Build RSI

Build MACD

Build OBV

Add VWAP

Add Volume Spike Detector (quantile-based)

Add “Trend State” classifier (bull/bear/flat)

📌 Issue 3 — Divergence Detection
Tasks:

Detect Bullish divergence (price LL + RSI HL)

Detect Bearish divergence (price HH + RSI LH)

MACD histogram divergence

Store divergence labels in dataset

Backtest divergence accuracy

📌 Issue 4 — News + Sentiment Engine
Tasks:

Scrape crypto/forex news (API or RSS)

Add economic calendar events (NFP, CPI, FOMC)

NLP sentiment using transformers (e.g., FinBERT)

Map news timestamps to price data

Build numerical sentiment score per candle

Detect high-impact event windows

Add “no-trade zone” during extreme events

📌 Issue 5 — ML Feature Builder
Tasks:

Combine TA features + Sentiment features

Sliding window feature extraction

Convert divergence to boolean features

Add future return classification (label creation)

Store ML dataset in /ml/features/

📌 Issue 6 — Machine Learning Model
Tasks:

Build LSTM model

Build Temporal CNN (TCN) alternative

Build GRU model

Train on 60–120 days of data

Compare accuracy, F1, recall

Save best model

Add real-time inference wrapper

Add nightly retraining function

📌 Issue 7 — Reinforcement Learning (Optional but OP)
Tasks:

Build RL environment (gym-style)

Agent chooses: buy / sell / hold

Reward = profit − risk penalty

Train actor-critic model

Validate on unseen data

📌 Issue 8 — Signal Engine (Hybrid Logic)
This is your trend + divergence + ML hybrid logic.
Tasks:

Check trend state (EMA alignment)

Check divergence

Check ML probability > threshold

Check sentiment filter

Generate BUY/SELL/EXIT signals

Pass signals to execution engine

📌 Issue 9 — Risk Management Engine
Tasks:

ATR-based SL/TP

Max risk % per trade

Max daily loss

Max consecutive losses

News event avoidance filter

Break-even stop

Trailing SL

Drawdown alerts

📌 Issue 10 — Order Execution Engine
Tasks:

MT5 integration

Binance/Bybit integration

Open/close trades

Manage SL & TP dynamically

Add trade logs

Handle API disconnects

Failsafe to close all trades on bot crash

📌 Issue 11 — Backtesting Framework
Tasks:

Simulate strategy (TA + ML + sentiment)

Analyze win rate

Longest DD

Average RR

Equity curve

Compare strategies (divergence vs no-divergence)

Walk-forward optimization

📌 Issue 12 — Deployment Pipeline
Tasks:

Docker container

VPS install automation

Cron job that:

Runs bot live

Trains ML nightly

Live logging dashboard (Streamlit)

Telegram alerts (entry, exit, errors)

📌 Issue 13 — README + Documentation
Tasks:

Explain architecture

Explain ML model

Explain risk management

Setup instructions

Diagrams and flow charts

🚀 3. OPTIONAL ADVANCED MODULES (Highly Recommended)
These elevate the bot to institutional level:
🧪 Alpha Signals

Smart Money Concepts (MSB, BOS, FVG, MIT)

Liquidity sweeps

High/low structure detection

Volume imbalance detection

📡 Live Market Regime Classifier

Trend

Ranging

High volatility

Low volatility

News-driven market

👁️ ML-based Volatility Forecasting
Predict ATR for next hour
→ helps dynamic position sizing
📈 Portfolio Allocation Engine
If multiple pairs:

Risk budget per asset

Correlation filter

Max exposure rules

...